From Eye to Mind: brain2text Decoding Reveals the Neural Mechanisms of Visual Semantic Processing arxiv.org/abs/2503.22697

From Eye to Mind: brain2text Decoding Reveals the Neural Mechanisms of Visual Semantic Processing

Deciphering the neural mechanisms that transform sensory experiences into meaningful semantic representations is a fundamental challenge in cognitive neuroscience. While neuroimaging has mapped a distributed semantic network, the format and neural code of semantic content remain elusive, particularly for complex, naturalistic stimuli. Traditional brain decoding, focused on visual reconstruction, primarily captures low-level perceptual features, missing the deeper semantic essence guiding human cognition. Here, we introduce a paradigm shift by directly decoding fMRI signals into textual descriptions of viewed natural images. Our novel deep learning model, trained without visual input, achieves state-of-the-art semantic decoding performance, generating meaningful captions that capture the core semantic content of complex scenes. Neuroanatomical analysis reveals the critical role of higher-level visual regions, including MT+, ventral stream visual cortex, and inferior parietal cortex, in this semantic transformation. Category-specific decoding further demonstrates nuanced neural representations for semantic dimensions like animacy and motion. This text-based decoding approach provides a more direct and interpretable window into the brain's semantic encoding than visual reconstruction, offering a powerful new methodology for probing the neural basis of complex semantic processing, refining our understanding of the distributed semantic network, and potentially inspiring brain-inspired language models.

arXiv.org

Anti-pathogenic property of thermophile-fermented compost as a feed additive and its in vivo external diagnostic imaging in a fish model arxiv.org/abs/2503.22916

Anti-pathogenic property of thermophile-fermented compost as a feed additive and its in vivo external diagnostic imaging in a fish model

Fermentation of organisms for recycling is important for the efficient cycling of nitrogen and phosphorus resources for a sustainable society, but the functionality of fermented products needs to be evaluated. Here, we clarify the anti-pathogenic properties for fish of a compost-type feed additive fermented by thermophilic Bacillaceae using non-edible marine resources as raw materials. After prior administration of the compost extract to seabream as a fish model for 70 days, the mortality rate after 28 days of exposure to the fish pathogen Edwardsiella reached a maximum of 20%, although the rate was 60% without prior administration. Under such conditions, the serum complement activity of seabream increased, and the recovery time after anesthesia treatment was also fasten. Furthermore, texture and HSV analysis using field photos statistically visualized differences in the degree of smoothness and gloss of the fish body surface depending on the administration. These results suggest that thermophile-fermented compost is effective as a functional feed additive against fish disease infection, and that such soundness can be estimated by body surface analysis. This study provides a new perspective for the natural symbiosis industry, as well as for the utilization of field non-invasive diagnosis to efficiently estimate the quality of its production activities.

arXiv.org

Evaluation of respiratory disease hospitalisation forecasts using synthetic outbreak data arxiv.org/abs/2503.22494

Evaluation of respiratory disease hospitalisation forecasts using synthetic outbreak data

Forecasts of hospitalisations of infectious diseases play an important role for allocating healthcare resources during epidemics and pandemics. Large-scale analysis of model forecasts during the COVID-19 pandemic has shown that the model rank distribution with respect to accuracy is heterogeneous and that ensemble forecasts have the highest average accuracy. Building on that work we generated a maximally diverse synthetic dataset of 324 different hospitalisation time-series that correspond to different disease characteristics and public health responses. We evaluated forecasts from 14 component models and 6 different ensembles. Our results show that component model accuracy was heterogeneous and varied depending on the current rate of disease transmission. Going from 7 day to 14 day forecasts mechanistic models improved in relative accuracy compared to statistical models. A novel adaptive ensemble method outperforms all other ensembles, but is closely followed by a median ensemble. We also investigated the relationship between ensemble error and variability of component forecasts and show that the coefficient of variation is predictive of future error. Lastly, we validated the results on data from the COVID-19 pandemic in Sweden. Our findings have the potential to improve epidemic forecasting, in particular the ability to assign confidence to ensemble forecasts at the time of prediction based on component forecast variability.

arXiv.org

Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining arxiv.org/abs/2503.20817

Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining

The differentiation between pathological subtypes of non-small cell lung cancer (NSCLC) is an essential step in guiding treatment options and prognosis. However, current clinical practice relies on multi-step staining and labelling processes that are time-intensive and costly, requiring highly specialised expertise. In this study, we propose a label-free methodology that facilitates autofluorescence imaging of unstained NSCLC samples and deep learning (DL) techniques to distinguish between non-cancerous tissue, adenocarcinoma (AC), squamous cell carcinoma (SqCC), and other subtypes (OS). We conducted DL-based classification and generated virtual immunohistochemical (IHC) stains, including thyroid transcription factor-1 (TTF-1) for AC and p40 for SqCC, and evaluated these methods using two types of autofluorescence imaging: intensity imaging and lifetime imaging. The results demonstrate the exceptional ability of this approach for NSCLC subtype differentiation, achieving an area under the curve above 0.981 and 0.996 for binary- and multi-class classification. Furthermore, this approach produces clinical-grade virtual IHC staining which was blind-evaluated by three experienced thoracic pathologists. Our label-free NSCLC subtyping approach enables rapid and accurate diagnosis without conventional tissue processing and staining. Both strategies can significantly accelerate diagnostic workflows and support efficient lung cancer diagnosis, without compromising clinical decision-making.

arXiv.org

Pattern Formation as a Resilience Mechanism in Cancer Immunotherapy arxiv.org/abs/2503.20909

Pattern Formation as a Resilience Mechanism in Cancer Immunotherapy

Mathematical and computational modelling in oncology has played an increasingly important role in not only understanding the impact of various approaches to treatment on tumour growth, but in optimizing dosing regimens and aiding the development of treatment strategies. However, as with all modelling, only an approximation is made in the description of the biological and physical system. Here we show that tissue-scale spatial structure can have a profound impact on the resilience of tumours to immunotherapy using a classical model incorporating IL-2 compounds and effector cells as treatment parameters. Using linear stability analysis, numerical continuation, and direct simulations, we show that diffusing cancer cell populations can undergo pattern-forming (Turing) instabilities, leading to spatially-structured states that persist far into treatment regimes where the corresponding spatially homogeneous systems would uniformly predict a cancer-free state. These spatially-patterned states persist in a wide range of parameters, as well as under time-dependent treatment regimes. Incorporating treatment via domain boundaries can increase this resistance to treatment in the interior of the domain, further highlighting the importance of spatial modelling when designing treatment protocols informed by mathematical models. Counter-intuitively, this mechanism shows that increased effector cell mobility can increase the resilience of tumours to treatment. We conclude by discussing practical and theoretical considerations for understanding this kind of spatial resilience in other models of cancer treatment, in particular those incorporating more realistic spatial transport.

arXiv.org

Two for the Price of One: Integrating Large Language Models to Learn Biophysical Interactions arxiv.org/abs/2503.21017

Value of risk-contact data from digital contact monitoring apps in infectious disease modeling arxiv.org/abs/2503.21228

Value of risk-contact data from digital contact monitoring apps in infectious disease modeling

In this paper, we present a simple method to integrate risk-contact data, obtained via digital contact monitoring (DCM) apps, in conventional compartmental transmission models. During the recent COVID-19 pandemic, many such data have been collected for the first time via newly developed DCM apps. However, it is unclear what the added value of these data is, unlike that of traditionally collected data via, e.g., surveys during non-epidemic times. The core idea behind our method is to express the number of infectious individuals as a function of the proportion of contacts that were with infected individuals and use this number as a starting point to initialize the remaining compartments of the model. As an important consequence, using our method, we can estimate key indicators such as the effective reproduction number using only two types of daily aggregated contact information, namely the average number of contacts and the average number of those contacts that were with an infected individual. We apply our method to the recent COVID-19 epidemic in the Netherlands, using self-reported data from the health surveillance app COVID RADAR and proximity-based data from the contact tracing app CoronaMelder. For both data sources, our corresponding estimates of the effective reproduction number agree both in time and magnitude with estimates based on other more detailed data sources such as daily numbers of cases and hospitalizations. This suggests that the use of DCM data in transmission models, regardless of the precise data type and for example via our method, offers a promising alternative for estimating the state of an epidemic, especially when more detailed data are not available.

arXiv.org

Regulation of Dendritic Cell Function by Ermiaosan via the EP4-cAMP-CREB Signaling Pathway arxiv.org/abs/2503.21233

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